Elevated atmospheric CO<sub>2</sub> interacts with drought and competition to produce complex results in plant quality and subsequent microbial aquatic decomposition
Bibliographic record
Abstract
Human activities enhance the atmospheric CO2 concentration and modify climatic patterns, which could change the growth and quality of plant materials, as well as their subsequent decomposition, thus altering stream ecosystem functioning. These effects could be modulated by interactions between plant species differing in biological traits and competitive capacity. We cultivated the forb Trifolium pratense and the grass Agrostis capillaris under different CO2 concentration (ambient or elevated), water availability (control or drought), and competition (monoculture or mixture). The material thus grown was conditioned in a stream for microbial colonization, and its subsequent decomposition was measured in laboratory microcosms. Elevated CO2 reduced the quality of T. pratense but not that of A. capillaris. Water shortage limited plant quality but did not interact with CO2 concentration. Interspecific competition only affected nitrogen concentration in A. capillaris. Elevated CO2 did not affect decomposition rate, and A. capillaris, the species richer in nutrients, decomposed more slowly. Our results showed that the decomposition rates of plant materials grown under elevated CO2 are difficult to predict due to species-specific responses and interactions with other factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".